from datetime import datetime

print(f"Date of last update: {datetime.now().strftime('%d.%m.%Y, %H:%M')}")

Date of last update: 28.07.2025, 03:50

%load_ext autoreload
%autoreload 2


from IPython.display import display, Markdown
from mfnf import MFNF


def md(text):
    display(Markdown(text))


df = MFNF().aggregate_pageviews()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
from statsmodels.tsa.seasonal import seasonal_decompose


def show_trend_analysis(df):
    # Assuming `df` is your DataFrame
    # Ensure 'timestamp' is of datetime dtype
    df["timestamp"] = pd.to_datetime(df["timestamp"])

    # Filter out the current month
    current_date = datetime.now()
    df_filtered = df[
        (df["timestamp"].dt.year < current_date.year)
        | (
            (df["timestamp"].dt.year == current_date.year)
            & (df["timestamp"].dt.month < current_date.month)
        )
    ]

    # Set the 'timestamp' as the index
    df_filtered.set_index("timestamp", inplace=True)

    # Resample to get monthly view data
    monthly_views = df_filtered["views"].resample("ME").sum()

    # Decompose the series
    decomposition = seasonal_decompose(
        monthly_views.dropna(), model="additive", period=12
    )

    # Set style for seaborn
    sns.set_style("whitegrid")

    # Plot the monthly views using Seaborn
    plt.figure(figsize=(12, 6))
    sns.lineplot(x=monthly_views.index, y=monthly_views.values, marker="o", linewidth=2)
    plt.title("Total Views by Month (Excluding Current Month)")
    plt.xlabel("Month")
    plt.ylabel("Views")
    plt.xticks(rotation=45)
    plt.show()

    # Plot the decomposition
    fig, axs = plt.subplots(4, 1, figsize=(14, 12), sharex=True)

    decomposition.observed.plot(ax=axs[0], color="blue")
    axs[0].set_ylabel("Observed")
    axs[0].set_title("Observed Component")

    decomposition.trend.plot(ax=axs[1], color="orange")
    axs[1].set_ylabel("Trend")
    axs[1].set_title("Trend Component")

    decomposition.seasonal.plot(ax=axs[2], color="green")
    axs[2].set_ylabel("Seasonal")
    axs[2].set_title("Seasonal Component")

    decomposition.resid.plot(ax=axs[3], color="red")
    axs[3].set_ylabel("Residual")
    axs[3].set_title("Residual Component")

    plt.tight_layout()
    plt.show()


md("## <span id='total-views'>Trend analysis of total views per month</span>")
show_trend_analysis(df.copy())

from mfnf import books

for book in books:
    md(f"### {book}")
    show_trend_analysis(df[df["book_name"] == book].copy())

Trend analysis¶

Utility functions¶

Trend analysis of total views per month¶

Trend analysis per book¶

Analysis 1¶

Grundlagen der Mathematik¶

Lineare Algebra 1¶

Maßtheorie¶

Real Analysis¶

Linear algebra¶

Measure theory¶

License of this report¶